{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Fully-Connected Neural Nets\n",
    "In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation was simple but not very modular since the loss and gradient were computed in a single monolithic function. This is manageable for a simple two-layer network, but would become impractical as we move to bigger models. Ideally we want to build networks using a more modular design so that we can implement different layer types in isolation and then snap them together into models with different architectures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "In this exercise we will implement fully-connected networks using a more modular approach. For each layer we will implement a `forward` and a `backward` function. The `forward` function will receive inputs, weights, and other parameters and will return both an output and a `cache` object storing data needed for the backward pass, like this:\n",
    "\n",
    "```python\n",
    "def layer_forward(x, w):\n",
    "  \"\"\" Receive inputs x and weights w \"\"\"\n",
    "  # Do some computations ...\n",
    "  z = # ... some intermediate value\n",
    "  # Do some more computations ...\n",
    "  out = # the output\n",
    "   \n",
    "  cache = (x, w, z, out) # Values we need to compute gradients\n",
    "   \n",
    "  return out, cache\n",
    "```\n",
    "\n",
    "The backward pass will receive upstream derivatives and the `cache` object, and will return gradients with respect to the inputs and weights, like this:\n",
    "\n",
    "```python\n",
    "def layer_backward(dout, cache):\n",
    "  \"\"\"\n",
    "  Receive dout (derivative of loss with respect to outputs) and cache,\n",
    "  and compute derivative with respect to inputs.\n",
    "  \"\"\"\n",
    "  # Unpack cache values\n",
    "  x, w, z, out = cache\n",
    "  \n",
    "  # Use values in cache to compute derivatives\n",
    "  dx = # Derivative of loss with respect to x\n",
    "  dw = # Derivative of loss with respect to w\n",
    "  \n",
    "  return dx, dw\n",
    "```\n",
    "\n",
    "After implementing a bunch of layers this way, we will be able to easily combine them to build classifiers with different architectures.\n",
    "\n",
    "In addition to implementing fully-connected networks of arbitrary depth, we will also explore different update rules for optimization, and introduce Dropout as a regularizer and Batch/Layer Normalization as a tool to more efficiently optimize deep networks.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "from __future__ import print_function\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('X_train: ', (49000, 3, 32, 32))\n",
      "('y_train: ', (49000,))\n",
      "('X_val: ', (1000, 3, 32, 32))\n",
      "('y_val: ', (1000,))\n",
      "('X_test: ', (1000, 3, 32, 32))\n",
      "('y_test: ', (1000,))\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in list(data.items()):\n",
    "  print(('%s: ' % k, v.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Affine layer: foward\n",
    "Open the file `cs231n/layers.py` and implement the `affine_forward` function.\n",
    "\n",
    "Once you are done you can test your implementaion by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_forward function:\n",
      "difference:  9.769849468192957e-10\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_forward function\n",
    "\n",
    "num_inputs = 2\n",
    "input_shape = (4, 5, 6)\n",
    "output_dim = 3\n",
    "\n",
    "input_size = num_inputs * np.prod(input_shape)\n",
    "weight_size = output_dim * np.prod(input_shape)\n",
    "\n",
    "x = np.linspace(-0.1, 0.5, num=input_size).reshape(num_inputs, *input_shape)\n",
    "w = np.linspace(-0.2, 0.3, num=weight_size).reshape(np.prod(input_shape), output_dim)\n",
    "b = np.linspace(-0.3, 0.1, num=output_dim)\n",
    "\n",
    "out, _ = affine_forward(x, w, b)\n",
    "correct_out = np.array([[ 1.49834967,  1.70660132,  1.91485297],\n",
    "                        [ 3.25553199,  3.5141327,   3.77273342]])\n",
    "\n",
    "# Compare your output with ours. The error should be around e-9 or less.\n",
    "print('Testing affine_forward function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Affine layer: backward\n",
    "Now implement the `affine_backward` function and test your implementation using numeric gradient checking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_backward function:\n",
      "dx error:  5.399100368651805e-11\n",
      "dw error:  9.904211865398145e-11\n",
      "db error:  2.4122867568119087e-11\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_backward function\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 2, 3)\n",
    "w = np.random.randn(6, 5)\n",
    "b = np.random.randn(5)\n",
    "dout = np.random.randn(10, 5)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "_, cache = affine_forward(x, w, b)\n",
    "dx, dw, db = affine_backward(dout, cache)\n",
    "\n",
    "# The error should be around e-10 or less\n",
    "print('Testing affine_backward function:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReLU activation: forward\n",
    "Implement the forward pass for the ReLU activation function in the `relu_forward` function and test your implementation using the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_forward function:\n",
      "difference:  4.999999798022158e-08\n"
     ]
    }
   ],
   "source": [
    "# Test the relu_forward function\n",
    "\n",
    "x = np.linspace(-0.5, 0.5, num=12).reshape(3, 4)\n",
    "\n",
    "out, _ = relu_forward(x)\n",
    "correct_out = np.array([[ 0.,          0.,          0.,          0.,        ],\n",
    "                        [ 0.,          0.,          0.04545455,  0.13636364,],\n",
    "                        [ 0.22727273,  0.31818182,  0.40909091,  0.5,       ]])\n",
    "\n",
    "# Compare your output with ours. The error should be on the order of e-8\n",
    "print('Testing relu_forward function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReLU activation: backward\n",
    "Now implement the backward pass for the ReLU activation function in the `relu_backward` function and test your implementation using numeric gradient checking:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_backward function:\n",
      "dx error:  3.2756349136310288e-12\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 10)\n",
    "dout = np.random.randn(*x.shape)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: relu_forward(x)[0], x, dout)\n",
    "\n",
    "_, cache = relu_forward(x)\n",
    "dx = relu_backward(dout, cache)\n",
    "\n",
    "# The error should be on the order of e-12\n",
    "print('Testing relu_backward function:')\n",
    "print('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 1: \n",
    "\n",
    "We've only asked you to implement ReLU, but there are a number of different activation functions that one could use in neural networks, each with its pros and cons. In particular, an issue commonly seen with activation functions is getting zero (or close to zero) gradient flow during backpropagation. Which of the following activation functions have this problem? If you consider these functions in the one dimensional case, what types of input would lead to this behaviour?\n",
    "1. Sigmoid\n",
    "2. ReLU\n",
    "3. Leaky ReLU\n",
    "\n",
    "## Answer:\n",
    "[FILL THIS IN]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# \"Sandwich\" layers\n",
    "There are some common patterns of layers that are frequently used in neural nets. For example, affine layers are frequently followed by a ReLU nonlinearity. To make these common patterns easy, we define several convenience layers in the file `cs231n/layer_utils.py`.\n",
    "\n",
    "For now take a look at the `affine_relu_forward` and `affine_relu_backward` functions, and run the following to numerically gradient check the backward pass:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_relu_forward and affine_relu_backward:\n",
      "dx error:  2.299579177309368e-11\n",
      "dw error:  8.162011105764925e-11\n",
      "db error:  7.826724021458994e-12\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import affine_relu_forward, affine_relu_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 4)\n",
    "w = np.random.randn(12, 10)\n",
    "b = np.random.randn(10)\n",
    "dout = np.random.randn(2, 10)\n",
    "\n",
    "out, cache = affine_relu_forward(x, w, b)\n",
    "dx, dw, db = affine_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_relu_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_relu_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_relu_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "# Relative error should be around e-10 or less\n",
    "print('Testing affine_relu_forward and affine_relu_backward:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loss layers: Softmax and SVM\n",
    "You implemented these loss functions in the last assignment, so we'll give them to you for free here. You should still make sure you understand how they work by looking at the implementations in `cs231n/layers.py`.\n",
    "\n",
    "You can make sure that the implementations are correct by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing svm_loss:\n",
      "loss:  8.999602749096233\n",
      "dx error:  1.4021566006651672e-09\n",
      "\n",
      "Testing softmax_loss:\n",
      "loss:  2.302545844500738\n",
      "dx error:  9.384673161989355e-09\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "num_classes, num_inputs = 10, 50\n",
    "x = 0.001 * np.random.randn(num_inputs, num_classes)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: svm_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = svm_loss(x, y)\n",
    "\n",
    "# Test svm_loss function. Loss should be around 9 and dx error should be around the order of e-9\n",
    "print('Testing svm_loss:')\n",
    "print('loss: ', loss)\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: softmax_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = softmax_loss(x, y)\n",
    "\n",
    "# Test softmax_loss function. Loss should be close to 2.3 and dx error should be around e-8\n",
    "print('\\nTesting softmax_loss:')\n",
    "print('loss: ', loss)\n",
    "print('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Two-layer network\n",
    "In the previous assignment you implemented a two-layer neural network in a single monolithic class. Now that you have implemented modular versions of the necessary layers, you will reimplement the two layer network using these modular implementations.\n",
    "\n",
    "Open the file `cs231n/classifiers/fc_net.py` and complete the implementation of the `TwoLayerNet` class. This class will serve as a model for the other networks you will implement in this assignment, so read through it to make sure you understand the API. You can run the cell below to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing initialization ... \n",
      "Testing test-time forward pass ... \n",
      "Testing training loss (no regularization)\n",
      "Running numeric gradient check with reg =  0.0\n",
      "W1 relative error: 1.83e-08\n",
      "W2 relative error: 3.20e-10\n",
      "b1 relative error: 9.83e-09\n",
      "b2 relative error: 4.33e-10\n",
      "Running numeric gradient check with reg =  0.7\n",
      "W1 relative error: 2.53e-07\n",
      "W2 relative error: 2.85e-08\n",
      "b1 relative error: 1.56e-08\n",
      "b2 relative error: 9.09e-10\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H, C = 3, 5, 50, 7\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=N)\n",
    "\n",
    "std = 1e-3\n",
    "model = TwoLayerNet(input_dim=D, hidden_dim=H, num_classes=C, weight_scale=std)\n",
    "\n",
    "print('Testing initialization ... ')\n",
    "W1_std = abs(model.params['W1'].std() - std)\n",
    "b1 = model.params['b1']\n",
    "W2_std = abs(model.params['W2'].std() - std)\n",
    "b2 = model.params['b2']\n",
    "assert W1_std < std / 10, 'First layer weights do not seem right'\n",
    "assert np.all(b1 == 0), 'First layer biases do not seem right'\n",
    "assert W2_std < std / 10, 'Second layer weights do not seem right'\n",
    "assert np.all(b2 == 0), 'Second layer biases do not seem right'\n",
    "\n",
    "print('Testing test-time forward pass ... ')\n",
    "model.params['W1'] = np.linspace(-0.7, 0.3, num=D*H).reshape(D, H)\n",
    "model.params['b1'] = np.linspace(-0.1, 0.9, num=H)\n",
    "model.params['W2'] = np.linspace(-0.3, 0.4, num=H*C).reshape(H, C)\n",
    "model.params['b2'] = np.linspace(-0.9, 0.1, num=C)\n",
    "X = np.linspace(-5.5, 4.5, num=N*D).reshape(D, N).T\n",
    "scores = model.loss(X)\n",
    "correct_scores = np.asarray(\n",
    "  [[11.53165108,  12.2917344,   13.05181771,  13.81190102,  14.57198434, 15.33206765,  16.09215096],\n",
    "   [12.05769098,  12.74614105,  13.43459113,  14.1230412,   14.81149128, 15.49994135,  16.18839143],\n",
    "   [12.58373087,  13.20054771,  13.81736455,  14.43418138,  15.05099822, 15.66781506,  16.2846319 ]])\n",
    "scores_diff = np.abs(scores - correct_scores).sum()\n",
    "assert scores_diff < 1e-6, 'Problem with test-time forward pass'\n",
    "\n",
    "print('Testing training loss (no regularization)')\n",
    "y = np.asarray([0, 5, 1])\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 3.4702243556\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with training-time loss'\n",
    "\n",
    "model.reg = 1.0\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 26.5948426952\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with regularization loss'\n",
    "\n",
    "# Errors should be around e-7 or less\n",
    "for reg in [0.0, 0.7]:\n",
    "  print('Running numeric gradient check with reg = ', reg)\n",
    "  model.reg = reg\n",
    "  loss, grads = model.loss(X, y)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solver\n",
    "In the previous assignment, the logic for training models was coupled to the models themselves. Following a more modular design, for this assignment we have split the logic for training models into a separate class.\n",
    "\n",
    "Open the file `cs231n/solver.py` and read through it to familiarize yourself with the API. After doing so, use a `Solver` instance to train a `TwoLayerNet` that achieves at least `50%` accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 4900) loss: 2.304060\n",
      "(Epoch 0 / 10) train acc: 0.116000; val_acc: 0.094000\n",
      "(Iteration 101 / 4900) loss: 1.829613\n",
      "(Iteration 201 / 4900) loss: 1.857390\n",
      "(Iteration 301 / 4900) loss: 1.744448\n",
      "(Iteration 401 / 4900) loss: 1.420187\n",
      "(Epoch 1 / 10) train acc: 0.407000; val_acc: 0.422000\n",
      "(Iteration 501 / 4900) loss: 1.565913\n",
      "(Iteration 601 / 4900) loss: 1.700510\n",
      "(Iteration 701 / 4900) loss: 1.732213\n",
      "(Iteration 801 / 4900) loss: 1.688361\n",
      "(Iteration 901 / 4900) loss: 1.439529\n",
      "(Epoch 2 / 10) train acc: 0.497000; val_acc: 0.468000\n",
      "(Iteration 1001 / 4900) loss: 1.385772\n",
      "(Iteration 1101 / 4900) loss: 1.278401\n",
      "(Iteration 1201 / 4900) loss: 1.641580\n",
      "(Iteration 1301 / 4900) loss: 1.438847\n",
      "(Iteration 1401 / 4900) loss: 1.172536\n",
      "(Epoch 3 / 10) train acc: 0.490000; val_acc: 0.466000\n",
      "(Iteration 1501 / 4900) loss: 1.346286\n",
      "(Iteration 1601 / 4900) loss: 1.268492\n",
      "(Iteration 1701 / 4900) loss: 1.318215\n",
      "(Iteration 1801 / 4900) loss: 1.395750\n",
      "(Iteration 1901 / 4900) loss: 1.338233\n",
      "(Epoch 4 / 10) train acc: 0.532000; val_acc: 0.497000\n",
      "(Iteration 2001 / 4900) loss: 1.343165\n",
      "(Iteration 2101 / 4900) loss: 1.393173\n",
      "(Iteration 2201 / 4900) loss: 1.276734\n",
      "(Iteration 2301 / 4900) loss: 1.287951\n",
      "(Iteration 2401 / 4900) loss: 1.352778\n",
      "(Epoch 5 / 10) train acc: 0.525000; val_acc: 0.475000\n",
      "(Iteration 2501 / 4900) loss: 1.390234\n",
      "(Iteration 2601 / 4900) loss: 1.276361\n",
      "(Iteration 2701 / 4900) loss: 1.111768\n",
      "(Iteration 2801 / 4900) loss: 1.271688\n",
      "(Iteration 2901 / 4900) loss: 1.272039\n",
      "(Epoch 6 / 10) train acc: 0.546000; val_acc: 0.509000\n",
      "(Iteration 3001 / 4900) loss: 1.304489\n",
      "(Iteration 3101 / 4900) loss: 1.346667\n",
      "(Iteration 3201 / 4900) loss: 1.325510\n",
      "(Iteration 3301 / 4900) loss: 1.392728\n",
      "(Iteration 3401 / 4900) loss: 1.402001\n",
      "(Epoch 7 / 10) train acc: 0.567000; val_acc: 0.505000\n",
      "(Iteration 3501 / 4900) loss: 1.319024\n",
      "(Iteration 3601 / 4900) loss: 1.153287\n",
      "(Iteration 3701 / 4900) loss: 1.180922\n",
      "(Iteration 3801 / 4900) loss: 1.093164\n",
      "(Iteration 3901 / 4900) loss: 1.135902\n",
      "(Epoch 8 / 10) train acc: 0.568000; val_acc: 0.490000\n",
      "(Iteration 4001 / 4900) loss: 1.191735\n",
      "(Iteration 4101 / 4900) loss: 1.359396\n",
      "(Iteration 4201 / 4900) loss: 1.227283\n",
      "(Iteration 4301 / 4900) loss: 1.024113\n",
      "(Iteration 4401 / 4900) loss: 1.327583\n",
      "(Epoch 9 / 10) train acc: 0.592000; val_acc: 0.504000\n",
      "(Iteration 4501 / 4900) loss: 0.963330\n",
      "(Iteration 4601 / 4900) loss: 1.445619\n",
      "(Iteration 4701 / 4900) loss: 1.007542\n",
      "(Iteration 4801 / 4900) loss: 1.005175\n",
      "(Epoch 10 / 10) train acc: 0.611000; val_acc: 0.512000\n"
     ]
    }
   ],
   "source": [
    "model = TwoLayerNet()\n",
    "solver = None\n",
    "\n",
    "##############################################################################\n",
    "# TODO: Use a Solver instance to train a TwoLayerNet that achieves at least  #\n",
    "# 50% accuracy on the validation set.                                        #\n",
    "##############################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "solver = Solver(model, data, update_rule='sgd', \n",
    "                optim_config={'learning_rate':1e-3,}, \n",
    "                lr_decay=0.95, num_epochs=10, \n",
    "                batch_size=100, print_every=100)\n",
    "solver.train()\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "##############################################################################\n",
    "#                             END OF YOUR CODE                               #\n",
    "##############################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this cell to visualize training loss and train / val accuracy\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.title('Accuracy')\n",
    "plt.plot(solver.train_acc_history, '-o', label='train')\n",
    "plt.plot(solver.val_acc_history, '-o', label='val')\n",
    "plt.plot([0.5] * len(solver.val_acc_history), 'k--')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(loc='lower right')\n",
    "plt.gcf().set_size_inches(15, 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multilayer network\n",
    "Next you will implement a fully-connected network with an arbitrary number of hidden layers.\n",
    "\n",
    "Read through the `FullyConnectedNet` class in the file `cs231n/classifiers/fc_net.py`.\n",
    "\n",
    "Implement the initialization, the forward pass, and the backward pass. For the moment don't worry about implementing dropout or batch/layer normalization; we will add those features soon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial loss and gradient check\n",
    "\n",
    "As a sanity check, run the following to check the initial loss and to gradient check the network both with and without regularization. Do the initial losses seem reasonable?\n",
    "\n",
    "For gradient checking, you should expect to see errors around 1e-7 or less."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.300479089768492\n",
      "W1 relative error: 1.03e-07\n",
      "W2 relative error: 2.21e-05\n",
      "W3 relative error: 4.56e-07\n",
      "b1 relative error: 4.66e-09\n",
      "b2 relative error: 2.09e-09\n",
      "b3 relative error: 1.69e-10\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  7.052114776533016\n",
      "W1 relative error: 6.86e-09\n",
      "W2 relative error: 3.52e-08\n",
      "W3 relative error: 2.62e-08\n",
      "b1 relative error: 1.48e-08\n",
      "b2 relative error: 1.72e-09\n",
      "b3 relative error: 2.38e-10\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for reg in [0, 3.14]:\n",
    "  print('Running check with reg = ', reg)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64)\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print('Initial loss: ', loss)\n",
    "  \n",
    "  # Most of the errors should be on the order of e-7 or smaller.   \n",
    "  # NOTE: It is fine however to see an error for W2 on the order of e-5\n",
    "  # for the check when reg = 0.0\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As another sanity check, make sure you can overfit a small dataset of 50 images. First we will try a three-layer network with 100 units in each hidden layer. In the following cell, tweak the **learning rate** and **weight initialization scale** to overfit and achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 188.174619\n",
      "(Epoch 0 / 20) train acc: 0.220000; val_acc: 0.119000\n",
      "(Epoch 1 / 20) train acc: 0.380000; val_acc: 0.150000\n",
      "(Epoch 2 / 20) train acc: 0.320000; val_acc: 0.156000\n",
      "(Epoch 3 / 20) train acc: 0.660000; val_acc: 0.153000\n",
      "(Epoch 4 / 20) train acc: 0.780000; val_acc: 0.172000\n",
      "(Epoch 5 / 20) train acc: 0.880000; val_acc: 0.174000\n",
      "(Iteration 11 / 40) loss: 5.796408\n",
      "(Epoch 6 / 20) train acc: 0.900000; val_acc: 0.176000\n",
      "(Epoch 7 / 20) train acc: 0.960000; val_acc: 0.179000\n",
      "(Epoch 8 / 20) train acc: 0.980000; val_acc: 0.179000\n",
      "(Epoch 9 / 20) train acc: 0.980000; val_acc: 0.179000\n",
      "(Epoch 10 / 20) train acc: 0.980000; val_acc: 0.179000\n",
      "(Iteration 21 / 40) loss: 0.000000\n",
      "(Epoch 11 / 20) train acc: 0.980000; val_acc: 0.179000\n",
      "(Epoch 12 / 20) train acc: 0.980000; val_acc: 0.172000\n",
      "(Epoch 13 / 20) train acc: 0.980000; val_acc: 0.172000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.170000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.170000\n",
      "(Iteration 31 / 40) loss: 0.000000\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.170000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.170000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.170000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.170000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.170000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a three-layer Net to overfit 50 training examples by \n",
    "# tweaking just the learning rate and initialization scale.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 1e-1   # Experiment with this!\n",
    "learning_rate = 1e-3  # Experiment with this!\n",
    "model = FullyConnectedNet([100, 100],\n",
    "              weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now try to use a five-layer network with 100 units on each layer to overfit 50 training examples. Again, you will have to adjust the learning rate and weight initialization scale, but you should be able to achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 131.226423\n",
      "(Epoch 0 / 20) train acc: 0.240000; val_acc: 0.093000\n",
      "(Epoch 1 / 20) train acc: 0.160000; val_acc: 0.113000\n",
      "(Epoch 2 / 20) train acc: 0.260000; val_acc: 0.103000\n",
      "(Epoch 3 / 20) train acc: 0.320000; val_acc: 0.121000\n",
      "(Epoch 4 / 20) train acc: 0.460000; val_acc: 0.118000\n",
      "(Epoch 5 / 20) train acc: 0.780000; val_acc: 0.120000\n",
      "(Iteration 11 / 40) loss: 2.541547\n",
      "(Epoch 6 / 20) train acc: 0.880000; val_acc: 0.122000\n",
      "(Epoch 7 / 20) train acc: 0.900000; val_acc: 0.117000\n",
      "(Epoch 8 / 20) train acc: 0.940000; val_acc: 0.119000\n",
      "(Epoch 9 / 20) train acc: 0.980000; val_acc: 0.118000\n",
      "(Epoch 10 / 20) train acc: 1.000000; val_acc: 0.124000\n",
      "(Iteration 21 / 40) loss: 0.019619\n",
      "(Epoch 11 / 20) train acc: 1.000000; val_acc: 0.124000\n",
      "(Epoch 12 / 20) train acc: 1.000000; val_acc: 0.123000\n",
      "(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.122000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.122000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.121000\n",
      "(Iteration 31 / 40) loss: 0.000826\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.121000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.121000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.121000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.121000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.121000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a five-layer Net to overfit 50 training examples by \n",
    "# tweaking just the learning rate and initialization scale.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "learning_rate = 2e-3  # Experiment with this!\n",
    "weight_scale = 1e-1   # Experiment with this!\n",
    "model = FullyConnectedNet([100, 100, 100, 100],\n",
    "                weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 2: \n",
    "Did you notice anything about the comparative difficulty of training the three-layer net vs training the five layer net? In particular, based on your experience, which network seemed more sensitive to the initialization scale? Why do you think that is the case?\n",
    "\n",
    "## Answer:\n",
    "[FILL THIS IN]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Update rules\n",
    "So far we have used vanilla stochastic gradient descent (SGD) as our update rule. More sophisticated update rules can make it easier to train deep networks. We will implement a few of the most commonly used update rules and compare them to vanilla SGD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SGD+Momentum\n",
    "Stochastic gradient descent with momentum is a widely used update rule that tends to make deep networks converge faster than vanilla stochastic gradient descent. See the Momentum Update section at http://cs231n.github.io/neural-networks-3/#sgd for more information.\n",
    "\n",
    "Open the file `cs231n/optim.py` and read the documentation at the top of the file to make sure you understand the API. Implement the SGD+momentum update rule in the function `sgd_momentum` and run the following to check your implementation. You should see errors less than e-8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  8.882347033505819e-09\n",
      "velocity error:  4.269287743278663e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.optim import sgd_momentum\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-3, 'velocity': v}\n",
    "next_w, _ = sgd_momentum(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [ 0.1406,      0.20738947,  0.27417895,  0.34096842,  0.40775789],\n",
    "  [ 0.47454737,  0.54133684,  0.60812632,  0.67491579,  0.74170526],\n",
    "  [ 0.80849474,  0.87528421,  0.94207368,  1.00886316,  1.07565263],\n",
    "  [ 1.14244211,  1.20923158,  1.27602105,  1.34281053,  1.4096    ]])\n",
    "expected_velocity = np.asarray([\n",
    "  [ 0.5406,      0.55475789,  0.56891579, 0.58307368,  0.59723158],\n",
    "  [ 0.61138947,  0.62554737,  0.63970526,  0.65386316,  0.66802105],\n",
    "  [ 0.68217895,  0.69633684,  0.71049474,  0.72465263,  0.73881053],\n",
    "  [ 0.75296842,  0.76712632,  0.78128421,  0.79544211,  0.8096    ]])\n",
    "\n",
    "# Should see relative errors around e-8 or less\n",
    "print('next_w error: ', rel_error(next_w, expected_next_w))\n",
    "print('velocity error: ', rel_error(expected_velocity, config['velocity']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have done so, run the following to train a six-layer network with both SGD and SGD+momentum. You should see the SGD+momentum update rule converge faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  sgd\n",
      "(Iteration 1 / 200) loss: 3.348916\n",
      "(Epoch 0 / 5) train acc: 0.093000; val_acc: 0.110000\n",
      "(Iteration 11 / 200) loss: 2.300108\n",
      "(Iteration 21 / 200) loss: 2.196804\n",
      "(Iteration 31 / 200) loss: 2.094425\n",
      "(Epoch 1 / 5) train acc: 0.265000; val_acc: 0.244000\n",
      "(Iteration 41 / 200) loss: 2.038850\n",
      "(Iteration 51 / 200) loss: 2.102217\n",
      "(Iteration 61 / 200) loss: 1.980477\n",
      "(Iteration 71 / 200) loss: 1.858254\n",
      "(Epoch 2 / 5) train acc: 0.329000; val_acc: 0.289000\n",
      "(Iteration 81 / 200) loss: 2.039095\n",
      "(Iteration 91 / 200) loss: 1.958172\n",
      "(Iteration 101 / 200) loss: 2.026073\n",
      "(Iteration 111 / 200) loss: 1.871741\n",
      "(Epoch 3 / 5) train acc: 0.344000; val_acc: 0.326000\n",
      "(Iteration 121 / 200) loss: 2.037131\n",
      "(Iteration 131 / 200) loss: 1.886442\n",
      "(Iteration 141 / 200) loss: 1.861478\n",
      "(Iteration 151 / 200) loss: 1.807977\n",
      "(Epoch 4 / 5) train acc: 0.389000; val_acc: 0.317000\n",
      "(Iteration 161 / 200) loss: 1.847632\n",
      "(Iteration 171 / 200) loss: 1.772387\n",
      "(Iteration 181 / 200) loss: 1.764182\n",
      "(Iteration 191 / 200) loss: 1.682020\n",
      "(Epoch 5 / 5) train acc: 0.380000; val_acc: 0.334000\n",
      "\n",
      "running with  sgd_momentum\n",
      "(Iteration 1 / 200) loss: 2.928477\n",
      "(Epoch 0 / 5) train acc: 0.106000; val_acc: 0.095000\n",
      "(Iteration 11 / 200) loss: 2.273086\n",
      "(Iteration 21 / 200) loss: 2.110301\n",
      "(Iteration 31 / 200) loss: 2.089158\n",
      "(Epoch 1 / 5) train acc: 0.269000; val_acc: 0.278000\n",
      "(Iteration 41 / 200) loss: 1.976984\n",
      "(Iteration 51 / 200) loss: 1.885742\n",
      "(Iteration 61 / 200) loss: 1.823514\n",
      "(Iteration 71 / 200) loss: 1.709423\n",
      "(Epoch 2 / 5) train acc: 0.366000; val_acc: 0.303000\n",
      "(Iteration 81 / 200) loss: 1.724025\n",
      "(Iteration 91 / 200) loss: 1.676847\n",
      "(Iteration 101 / 200) loss: 1.814341\n",
      "(Iteration 111 / 200) loss: 1.756369\n",
      "(Epoch 3 / 5) train acc: 0.433000; val_acc: 0.324000\n",
      "(Iteration 121 / 200) loss: 1.567477\n",
      "(Iteration 131 / 200) loss: 1.502650\n",
      "(Iteration 141 / 200) loss: 1.467669\n",
      "(Iteration 151 / 200) loss: 1.500682\n",
      "(Epoch 4 / 5) train acc: 0.475000; val_acc: 0.320000\n",
      "(Iteration 161 / 200) loss: 1.453198\n",
      "(Iteration 171 / 200) loss: 1.396093\n",
      "(Iteration 181 / 200) loss: 1.421664\n",
      "(Iteration 191 / 200) loss: 1.165524\n",
      "(Epoch 5 / 5) train acc: 0.538000; val_acc: 0.338000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lsm/miniconda3/envs/cs231n/lib/python3.7/site-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: \n",
      "Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  \"Adding an axes using the same arguments as a previous axes \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_train = 4000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "solvers = {}\n",
    "\n",
    "for update_rule in ['sgd', 'sgd_momentum']:\n",
    "  print('running with ', update_rule)\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': 5e-3,\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print()\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in solvers.items():\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=\"loss_%s\" % update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=\"train_acc_%s\" % update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=\"val_acc_%s\" % update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RMSProp and Adam\n",
    "RMSProp [1] and Adam [2] are update rules that set per-parameter learning rates by using a running average of the second moments of gradients.\n",
    "\n",
    "In the file `cs231n/optim.py`, implement the RMSProp update rule in the `rmsprop` function and implement the Adam update rule in the `adam` function, and check your implementations using the tests below.\n",
    "\n",
    "**NOTE:** Please implement the _complete_ Adam update rule (with the bias correction mechanism), not the first simplified version mentioned in the course notes. \n",
    "\n",
    "[1] Tijmen Tieleman and Geoffrey Hinton. \"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.\" COURSERA: Neural Networks for Machine Learning 4 (2012).\n",
    "\n",
    "[2] Diederik Kingma and Jimmy Ba, \"Adam: A Method for Stochastic Optimization\", ICLR 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  9.524687511038133e-08\n",
      "cache error:  2.6477955807156126e-09\n"
     ]
    }
   ],
   "source": [
    "# Test RMSProp implementation\n",
    "from cs231n.optim import rmsprop\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "cache = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'cache': cache}\n",
    "next_w, _ = rmsprop(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.39223849, -0.34037513, -0.28849239, -0.23659121, -0.18467247],\n",
    "  [-0.132737,   -0.08078555, -0.02881884,  0.02316247,  0.07515774],\n",
    "  [ 0.12716641,  0.17918792,  0.23122175,  0.28326742,  0.33532447],\n",
    "  [ 0.38739248,  0.43947102,  0.49155973,  0.54365823,  0.59576619]])\n",
    "expected_cache = np.asarray([\n",
    "  [ 0.5976,      0.6126277,   0.6277108,   0.64284931,  0.65804321],\n",
    "  [ 0.67329252,  0.68859723,  0.70395734,  0.71937285,  0.73484377],\n",
    "  [ 0.75037008,  0.7659518,   0.78158892,  0.79728144,  0.81302936],\n",
    "  [ 0.82883269,  0.84469141,  0.86060554,  0.87657507,  0.8926    ]])\n",
    "\n",
    "# You should see relative errors around e-7 or less\n",
    "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print('cache error: ', rel_error(expected_cache, config['cache']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  1.1395691798535431e-07\n",
      "v error:  4.208314038113071e-09\n",
      "m error:  4.214963193114416e-09\n"
     ]
    }
   ],
   "source": [
    "# Test Adam implementation\n",
    "from cs231n.optim import adam\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "m = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.7, 0.5, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'm': m, 'v': v, 't': 5}\n",
    "next_w, _ = adam(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.40094747, -0.34836187, -0.29577703, -0.24319299, -0.19060977],\n",
    "  [-0.1380274,  -0.08544591, -0.03286534,  0.01971428,  0.0722929],\n",
    "  [ 0.1248705,   0.17744702,  0.23002243,  0.28259667,  0.33516969],\n",
    "  [ 0.38774145,  0.44031188,  0.49288093,  0.54544852,  0.59801459]])\n",
    "expected_v = np.asarray([\n",
    "  [ 0.69966,     0.68908382,  0.67851319,  0.66794809,  0.65738853,],\n",
    "  [ 0.64683452,  0.63628604,  0.6257431,   0.61520571,  0.60467385,],\n",
    "  [ 0.59414753,  0.58362676,  0.57311152,  0.56260183,  0.55209767,],\n",
    "  [ 0.54159906,  0.53110598,  0.52061845,  0.51013645,  0.49966,   ]])\n",
    "expected_m = np.asarray([\n",
    "  [ 0.48,        0.49947368,  0.51894737,  0.53842105,  0.55789474],\n",
    "  [ 0.57736842,  0.59684211,  0.61631579,  0.63578947,  0.65526316],\n",
    "  [ 0.67473684,  0.69421053,  0.71368421,  0.73315789,  0.75263158],\n",
    "  [ 0.77210526,  0.79157895,  0.81105263,  0.83052632,  0.85      ]])\n",
    "\n",
    "# You should see relative errors around e-7 or less\n",
    "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print('v error: ', rel_error(expected_v, config['v']))\n",
    "print('m error: ', rel_error(expected_m, config['m']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have debugged your RMSProp and Adam implementations, run the following to train a pair of deep networks using these new update rules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  adam\n",
      "(Iteration 1 / 200) loss: 3.173906\n",
      "(Epoch 0 / 5) train acc: 0.099000; val_acc: 0.101000\n",
      "(Iteration 11 / 200) loss: 2.120522\n",
      "(Iteration 21 / 200) loss: 1.858085\n",
      "(Iteration 31 / 200) loss: 2.008590\n",
      "(Epoch 1 / 5) train acc: 0.360000; val_acc: 0.329000\n",
      "(Iteration 41 / 200) loss: 1.951958\n",
      "(Iteration 51 / 200) loss: 1.767173\n",
      "(Iteration 61 / 200) loss: 1.589701\n",
      "(Iteration 71 / 200) loss: 1.502034\n",
      "(Epoch 2 / 5) train acc: 0.452000; val_acc: 0.338000\n",
      "(Iteration 81 / 200) loss: 1.591386\n",
      "(Iteration 91 / 200) loss: 1.599136\n",
      "(Iteration 101 / 200) loss: 1.436616\n",
      "(Iteration 111 / 200) loss: 1.426626\n",
      "(Epoch 3 / 5) train acc: 0.506000; val_acc: 0.363000\n",
      "(Iteration 121 / 200) loss: 1.522407\n",
      "(Iteration 131 / 200) loss: 1.401222\n",
      "(Iteration 141 / 200) loss: 1.626568\n",
      "(Iteration 151 / 200) loss: 1.410368\n",
      "(Epoch 4 / 5) train acc: 0.548000; val_acc: 0.377000\n",
      "(Iteration 161 / 200) loss: 1.442548\n",
      "(Iteration 171 / 200) loss: 1.327131\n",
      "(Iteration 181 / 200) loss: 1.326683\n",
      "(Iteration 191 / 200) loss: 1.168002\n",
      "(Epoch 5 / 5) train acc: 0.534000; val_acc: 0.394000\n",
      "\n",
      "running with  rmsprop\n",
      "(Iteration 1 / 200) loss: 2.669494\n",
      "(Epoch 0 / 5) train acc: 0.127000; val_acc: 0.125000\n",
      "(Iteration 11 / 200) loss: 2.211245\n",
      "(Iteration 21 / 200) loss: 1.978603\n",
      "(Iteration 31 / 200) loss: 1.889495\n",
      "(Epoch 1 / 5) train acc: 0.364000; val_acc: 0.278000\n",
      "(Iteration 41 / 200) loss: 1.746479\n",
      "(Iteration 51 / 200) loss: 1.801176\n",
      "(Iteration 61 / 200) loss: 1.696733\n",
      "(Iteration 71 / 200) loss: 1.795328\n",
      "(Epoch 2 / 5) train acc: 0.420000; val_acc: 0.323000\n",
      "(Iteration 81 / 200) loss: 1.691071\n",
      "(Iteration 91 / 200) loss: 1.592479\n",
      "(Iteration 101 / 200) loss: 1.673743\n",
      "(Iteration 111 / 200) loss: 1.571748\n",
      "(Epoch 3 / 5) train acc: 0.473000; val_acc: 0.337000\n",
      "(Iteration 121 / 200) loss: 1.566274\n",
      "(Iteration 131 / 200) loss: 1.429301\n",
      "(Iteration 141 / 200) loss: 1.510939\n",
      "(Iteration 151 / 200) loss: 1.521260\n",
      "(Epoch 4 / 5) train acc: 0.484000; val_acc: 0.357000\n",
      "(Iteration 161 / 200) loss: 1.424142\n",
      "(Iteration 171 / 200) loss: 1.503051\n",
      "(Iteration 181 / 200) loss: 1.322605\n",
      "(Iteration 191 / 200) loss: 1.334314\n",
      "(Epoch 5 / 5) train acc: 0.568000; val_acc: 0.353000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_rates = {'rmsprop': 1e-4, 'adam': 1e-3}\n",
    "for update_rule in ['adam', 'rmsprop']:\n",
    "  print('running with ', update_rule)\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': learning_rates[update_rule]\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print()\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in list(solvers.items()):\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 3:\n",
    "\n",
    "AdaGrad, like Adam, is a per-parameter optimization method that uses the following update rule:\n",
    "\n",
    "```\n",
    "cache += dw**2\n",
    "w += - learning_rate * dw / (np.sqrt(cache) + eps)\n",
    "```\n",
    "\n",
    "John notices that when he was training a network with AdaGrad that the updates became very small, and that his network was learning slowly. Using your knowledge of the AdaGrad update rule, why do you think the updates would become very small? Would Adam have the same issue?\n",
    "\n",
    "\n",
    "## Answer: \n",
    "[FILL THIS IN]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train a good model!\n",
    "Train the best fully-connected model that you can on CIFAR-10, storing your best model in the `best_model` variable. We require you to get at least 50% accuracy on the validation set using a fully-connected net.\n",
    "\n",
    "If you are careful it should be possible to get accuracies above 55%, but we don't require it for this part and won't assign extra credit for doing so. Later in the assignment we will ask you to train the best convolutional network that you can on CIFAR-10, and we would prefer that you spend your effort working on convolutional nets rather than fully-connected nets.\n",
    "\n",
    "You might find it useful to complete the `BatchNormalization.ipynb` and `Dropout.ipynb` notebooks before completing this part, since those techniques can help you train powerful models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weight_scales = 0.050000, learning_rates = 0.001000, batch_sizes = 200.000000\n",
      "(Iteration 1 / 7350) loss: 34.160067\n",
      "(Epoch 0 / 30) train acc: 0.108000; val_acc: 0.104000\n",
      "(Epoch 1 / 30) train acc: 0.391000; val_acc: 0.378000\n",
      "(Epoch 2 / 30) train acc: 0.427000; val_acc: 0.409000\n",
      "(Iteration 501 / 7350) loss: 1.970038\n",
      "(Epoch 3 / 30) train acc: 0.434000; val_acc: 0.422000\n",
      "(Epoch 4 / 30) train acc: 0.430000; val_acc: 0.416000\n",
      "(Iteration 1001 / 7350) loss: 1.920248\n",
      "(Epoch 5 / 30) train acc: 0.461000; val_acc: 0.421000\n",
      "(Epoch 6 / 30) train acc: 0.431000; val_acc: 0.424000\n",
      "(Iteration 1501 / 7350) loss: 1.828278\n",
      "(Epoch 7 / 30) train acc: 0.474000; val_acc: 0.443000\n",
      "(Epoch 8 / 30) train acc: 0.460000; val_acc: 0.416000\n",
      "(Iteration 2001 / 7350) loss: 1.662591\n",
      "(Epoch 9 / 30) train acc: 0.446000; val_acc: 0.410000\n",
      "(Epoch 10 / 30) train acc: 0.501000; val_acc: 0.493000\n",
      "(Iteration 2501 / 7350) loss: 1.646405\n",
      "(Epoch 11 / 30) train acc: 0.514000; val_acc: 0.466000\n",
      "(Epoch 12 / 30) train acc: 0.496000; val_acc: 0.486000\n",
      "(Iteration 3001 / 7350) loss: 1.448339\n",
      "(Epoch 13 / 30) train acc: 0.521000; val_acc: 0.508000\n",
      "(Epoch 14 / 30) train acc: 0.545000; val_acc: 0.506000\n",
      "(Iteration 3501 / 7350) loss: 1.618383\n",
      "(Epoch 15 / 30) train acc: 0.527000; val_acc: 0.513000\n",
      "(Epoch 16 / 30) train acc: 0.538000; val_acc: 0.490000\n",
      "(Iteration 4001 / 7350) loss: 1.612581\n",
      "(Epoch 17 / 30) train acc: 0.543000; val_acc: 0.498000\n",
      "(Epoch 18 / 30) train acc: 0.559000; val_acc: 0.517000\n",
      "(Iteration 4501 / 7350) loss: 1.483015\n",
      "(Epoch 19 / 30) train acc: 0.570000; val_acc: 0.529000\n",
      "(Epoch 20 / 30) train acc: 0.562000; val_acc: 0.530000\n",
      "(Iteration 5001 / 7350) loss: 1.418609\n",
      "(Epoch 21 / 30) train acc: 0.590000; val_acc: 0.528000\n",
      "(Epoch 22 / 30) train acc: 0.597000; val_acc: 0.540000\n",
      "(Iteration 5501 / 7350) loss: 1.556431\n",
      "(Epoch 23 / 30) train acc: 0.622000; val_acc: 0.531000\n",
      "(Epoch 24 / 30) train acc: 0.606000; val_acc: 0.533000\n",
      "(Iteration 6001 / 7350) loss: 1.344554\n",
      "(Epoch 25 / 30) train acc: 0.628000; val_acc: 0.550000\n",
      "(Epoch 26 / 30) train acc: 0.647000; val_acc: 0.533000\n",
      "(Iteration 6501 / 7350) loss: 1.390442\n",
      "(Epoch 27 / 30) train acc: 0.670000; val_acc: 0.528000\n",
      "(Epoch 28 / 30) train acc: 0.661000; val_acc: 0.556000\n",
      "(Iteration 7001 / 7350) loss: 1.012023\n",
      "(Epoch 29 / 30) train acc: 0.696000; val_acc: 0.527000\n",
      "(Epoch 30 / 30) train acc: 0.693000; val_acc: 0.525000\n",
      "val_acc = 0.529000\n",
      "=========================================================================\n",
      "Training took 0 hours 16 minutes 28 seconds\n"
     ]
    }
   ],
   "source": [
    "best_model = None\n",
    "best_val = -1\n",
    "################################################################################\n",
    "# TODO: Train the best FullyConnectedNet that you can on CIFAR-10. You might   #\n",
    "# find batch/layer normalization and dropout useful. Store your best model in  #\n",
    "# the best_model variable.                                                     #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "# Check train and val accuracy on the first iteration, the last\n",
    "# iteration, and at the end of each epoch.\n",
    "# Append loss(not train or val loss) per iteration\n",
    "import time\n",
    "tic = time.time()\n",
    "\n",
    "X_val= data['X_val']\n",
    "y_val= data['y_val']\n",
    "X_test= data['X_test']\n",
    "y_test= data['y_test']\n",
    "\n",
    "regularization_strength = 1e-2\n",
    "# weight_scales = [2.5e-2, 5e-2]\n",
    "# learning_rates = [1e-3, 3e-3, 1e-2]\n",
    "# batch_sizes = [100]\n",
    "weight_scales = [5e-2]\n",
    "learning_rates = [1e-3]\n",
    "batch_sizes = [200]\n",
    "for ws in weight_scales:\n",
    "    for lr in learning_rates:\n",
    "        for bs in batch_sizes:\n",
    "            model = FullyConnectedNet([600, 500, 400, 300, 200, 100],\n",
    "                                     weight_scale=ws,\n",
    "                                     reg = regularization_strength, dtype=np.float64,\n",
    "                                     dropout=1, normalization='batchnorm')\n",
    "            \n",
    "            solver = Solver(model, data,\n",
    "                print_every=500, num_epochs=30, batch_size=bs,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': lr,\n",
    "                },\n",
    "                lr_decay=0.95) # decay learning rate when epoch increase 1\n",
    "            print('weight_scales = %f, learning_rates = %f, batch_sizes = %f' % (ws, lr, bs))\n",
    "            solver.train()\n",
    "            y_val_pred = np.argmax(model.loss(X_val), axis=1)\n",
    "            val_acc = (y_val_pred == y_val).mean()\n",
    "            print('val_acc = %f' % (val_acc))\n",
    "            print('=========================================================================')\n",
    "            if val_acc > best_val:\n",
    "                best_val = val_acc\n",
    "                best_model = model\n",
    "\n",
    "toc = time.time()\n",
    "total_seconds = int(toc - tic)\n",
    "hours = total_seconds // 3600\n",
    "minutes = (total_seconds - hours * 3600) // 60\n",
    "seconds = total_seconds - hours * 3600 - minutes * 60\n",
    "print('Training took %d hours %d minutes %d seconds' % (hours, minutes, seconds))\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test your model!\n",
    "Run your best model on the validation and test sets. You should achieve above 50% accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation set accuracy:  0.529\n",
      "Test set accuracy:  0.505\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = np.argmax(best_model.loss(data['X_test']), axis=1)\n",
    "y_val_pred = np.argmax(best_model.loss(data['X_val']), axis=1)\n",
    "print('Validation set accuracy: ', (y_val_pred == data['y_val']).mean())\n",
    "print('Test set accuracy: ', (y_test_pred == data['y_test']).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
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   ],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(solver.loss_history)\n",
    "plt.title('Loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Loss')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(solver.train_acc_history, label='train')\n",
    "plt.plot(solver.val_acc_history, label='val')\n",
    "plt.title('Classification accuracy history')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Clasification accuracy')\n",
    "plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()\n",
    "plt.show()"
   ]
  }
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